Similar to unsupervised VO,
Visual-inertial odometry can also be solved in a self-supervised fashion using novel view synthesis. VIOLearner [1]} constructs motion transformations from raw inertial data, and converts source images into target images with the camera matrix and depth maps via the Equation REF  mentioned in Section REF .
In addition, an online error correction module corrects the intermediate errors of the framework. The network parameters are recovered by optimizing a photometric loss.
Similarly, DeepVIO [2]} incorporates inertial data and stereo images into this unsupervised learning framework, and is trained with a dedicated loss to reconstruct trajectories in a global scale.
